Two-dimensional deconvolution methods are proposed to deblur optical coherence tomography images. One employs a two-dimensional deconvolution with a matrix given by the product of the longitudinal and transversal point-spread functions as its kernel, which can be taken as the general point-spread function of an optical coherence tomography system. The other uses two one-dimensional deconvolutions with the longitudinal and transversal point-spread functions successively. It is shown that the two deconvolution methods can deblur the experimentally obtained optical coherence tomography images effectively.

Early detection and diagnosis of oral cancer are critical for a better prognosis, but accurate and automatic identification is difficult using the available technologies. Optical coherence tomography (OCT) can be used as diagnostic aid due to the advantages of high resolution and non-invasion. We aim to evaluate deep-learning-based algorithms for OCT images to assist clinicians in oral cancer screening and diagnosis. An OCT data set was first established, including normal mucosa, precancerous lesion, and oral squamous cell carcinoma. Then, three kinds of convolutional neural networks (CNNs) were trained and evaluated by using four metrics (accuracy, precision, sensitivity, and specificity). Moreover, the CNN-based methods were compared against machine learning approaches through the same dataset. The results show the performance of CNNs, with a classification accuracy of up to 96.76%, is better than the machine-learning-based method with an accuracy of 92.52%. Moreover, visualization of lesions in OCT images was performed and the rationality and interpretability of the model for distinguishing different oral tissues were evaluated. It is proved that the automatic identification algorithm of OCT images based on deep learning has the potential to provide decision support for the effective screening and diagnosis of oral cancer.

Zebrafish are an important animal model, whose structure and function information can be used to study development, pathologic changes and genetic mutations. However, limited by the penetration depth, the available optical methods are difficult to image the whole-body zebrafish in juvenile and adult stages. Based on a home-made high-resolution polarization-sensitive optical coherence tomography (PS-OCT) system, we finished in vivo volumetric imaging for zebrafish, and various muscles can be clearly discerned by scanning from dorsal, ventral, and lateral directions. Besides structure information, polarization properties extracted from PS-OCT images provide abundant function information to distinguish different muscles. Furthermore, we found local retardation and local optic axis of zebrafish muscle are related to their composition and fiber orientation. We think high-resolution PS-OCT will be a promising tool in studying myopathy models of zebrafish.

Myocardial infarctions are most often caused by the so-called vulnerable plaques, usually featured as non-obstructive lesions with a lipid-rich necrotic core, thin-cap fibroatheroma, and large plaque size. The identification and quantification of these characteristics are the keys to evaluate plaque vulnerability. However, single modality intravascular methods, such as intravascular ultrasound, optical coherence tomography and photoacoustic, can hardly achieve all the comprehensive information to satisfy clinical needs. In this paper, for the first time, we developed a novel multi-spectral intravascular tri-modality (MS-IVTM) imaging system, which can perform 360° continuous rotation and pull-backing with a 0.9-mm miniature catheter and achieve simultaneous acquisition of both morphological characteristics and pathological compositions. Intravascular tri-modality imaging demonstrates the ability of our MS-IVTM system to provide macroscopic and microscopic structural information of the vessel wall, with identity and quantification of lipids with multi-wavelength excitation. This study offers clinicians and researchers a novel imaging tool to facilitate the accurate diagnosis of vulnerable atherosclerotic plaques. It also has the potential of clinical translations to help better identify and evaluate high-risk plaques during coronary interventions.

Depth-resolved optical attenuation coefficient is a valuable tissue parameter that complements the intensity-based structural information in optical coherent tomography (OCT) imaging. Herein we systematically analyzed the under- and over-estimation bias of existing depth-resolved methods when applied to real biological tissues, and then proposed a new algorithm that remedies these issues and accommodates general OCT data that contain incomplete decay and noise floor, thereby affording consistent estimation accuracy for practical biological samples of different scattering properties. Compared with other algorithms, our method demonstrates remarkably improved estimation accuracy and numerical robustness, as validated via numerical simulations and on experimental OCT data obtained from both silicone-TiO₂ phantoms and human ventral tongue leukoplakia samples.

Optical coherence tomography (OCT) is a noninvasive, high resolution, and real-time imaging technology that has been used in ophthalmology and other medical fields. Limited by the point spread function of OCT system, it is difficult to optimize its spatial resolution only based on hardware. Digital image processing methods, especially deep learning, provide great potential in super-resolving images. In this paper, the matched axial low resolution (LR) and high resolution OCT image pairs from actual OCT imaging are collected to generate the dataset by our home-made spectral domain OCT (SD-OCT) system. Several methods are selected to super-resolve LR OCT images. It is shown from the experimental results that the residual-in-residual dense block network (RRDBNet) trained with different loss functions performs the best super-resolution for OCT images, and it is demonstrated from the preliminary results that deep learning methods have good generalization and robustness between OCT systems. We believe deep learning methods have broad prospects in improving the quality of OCT images.